How is data stored in different partitions in a Cosmos DB container?
In Cosmos DB, data is stored across multiple partitions within a container. Each container is provisioned with a set number of throughput (measured in Request Units per Second or RUs) that determines the capacity available for processing data operations.
Here’s an overview of how data is stored in different partitions in a Cosmos DB container:
- Logical Partitioning: A container is divided into logical partitions. Each logical partition represents a distinct subset of data stored within the container. The logical partition is defined by a partition key, which is a property within each item.
- Physical Partitioning: Cosmos DB employs a distributed database architecture where logical partitions are mapped to physical partitions. The number of physical partitions is automatically managed by the system based on the container’s throughput requirements and storage size.
- Partition Key Hashing: When data is inserted or updated in the container, Cosmos DB uses a hashing function on the partition key value to determine the target physical partition for storing the data. The hash function ensures even distribution of data across the available physical partitions.
- Data Placement: Once the physical partition is determined, the data is stored within that partition. All items with the same partition key value are grouped together and stored within the same physical partition. This grouping allows for efficient retrieval of data when querying based on the partition key.
- Scalability and Load Balancing: Cosmos DB automatically manages the distribution of logical partitions across physical partitions to achieve load balancing and scalability. As the workload increases or decreases, Cosmos DB may dynamically split or merge physical partitions to optimize performance and distribute the data evenly.
- Performance Considerations: By distributing data across multiple partitions, Cosmos DB achieves parallelism, allowing for concurrent read and write operations on different partitions. However, it’s important to avoid “hot partitions” where a single partition receives a disproportionate amount of traffic, as it can impact performance. Careful choice of partition key and even data distribution help avoid hot partitions.
Overall, Cosmos DB’s partitioning model allows for efficient storage and retrieval of data by leveraging the distributed nature of the database. The use of logical and physical partitions, along with partition key hashing and automatic load balancing, enables horizontal scalability, high performance, and reliable data storage in Cosmos DB.